Child Health and Economic Prosperity: A Global Analysis of ORT Coverage
Author
Vishal - A00046486 | DCU Business School |
1 A Story of Care and Resilience
In a world often focused on groundbreaking medical advancements, the simple yet life-saving power of a mixture of salt, sugar, and clean water is frequently overlooked. Diarrhoea remains one of the leading causes of death among children under five, particularly in low-income regions. The tragedy lies not just in the illness itself, but in the fact that the treatment — Oral Rehydration Therapy (ORT) — is well-known, highly effective, and affordable. Yet, millions of children still go without it due to barriers such as poverty, misinformation, and weak healthcare systems. This report sheds light on these hidden struggles, using global data to represent the lives of children, families, and healthcare workers. Through these visuals, you’ll uncover trends of progress, areas of disparity, and the significant impact of community health initiatives. Behind every data point is a child whose life was saved — or could have been — by this simple solution. This isn’t just a data analysis; it’s a story of resilience, equity, and the collective ability to save lives — one sip at a time.
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import polars as plimport pandas as pdimport plotly.express as pximport plotly.graph_objects as goimport geopandas as gpdimport statsmodels.api as smfrom plotly.subplots import make_subplots# note these files are already cleaned and the null values are removed from them try: diarrhoea_df = pl.read_csv("cleaned_data_1_no_null.csv") metadata_df = pl.read_csv("cleaned_data_0_no_null.csv")exceptFileNotFoundErroras e:print(f"Error loading data files: {e}")print("Please ensure both CSV files are in the working directory") exit()sample_df = diarrhoea_df.to_pandas()df = diarrhoea_df.join( metadata_df, on=["country"], how="inner").filter( (pl.col("sex") =="Total") & (pl.col("time_period") == pl.col("year")))pdf = df.to_pandas()pdf = pdf.sort_values("time_period")
2 Key Indicators of Global Diarrhoea Treatment Efforts
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import plotly.graph_objects as go# just taking there average here avg_obs = sample_df['obs_value'].mean()fig1 = go.Figure(go.Indicator( mode="number", value=avg_obs, number={'valueformat': '.1f', 'font': {'size': 50, 'color': '#81c784'}}, title={"text": "Average ORT Coverage (%)", 'font': {'size': 20}}))fig1.update_layout(height=300, margin=dict(t=40, b=10, l=10, r=10))fig1.show()
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#this to how many we have record_count = sample_df.shape[0]fig2 = go.Figure(go.Indicator( mode="number", value=record_count, number={'font': {'size': 50, 'color': '#81c784'}}, title={"text": "Total Data Points", 'font': {'size': 20}}))fig2.update_layout(height=300, margin=dict(t=40, b=10, l=10, r=10))fig2.show()
Every color shift on this interactive world map tells a story of struggle, survival, disparity, and hope. It illustrates Global ORT Coverage Trends from 2000 to 2022, capturing the ongoing battle against diarrhoea in children under five. As you explore, each year unfolds new insights—some years bring progress and hope, while others remind us of the persistent challenges in providing essential healthcare.
The option to view the data by gender adds a deeper layer to the narrative, highlighting the gender disparities that still exist in healthcare access, urging us to strive for greater equity. Each data point on the map represents a child whose life could be saved with access to Oral Rehydration Therapy (ORT), a simple yet life-saving treatment.
This map is more than just a visualization of statistics; it’s a powerful reminder of our world’s realities and the work still needed. It calls on all of us to not just observe, but to understand and take action. Because at the heart of these data points are children who deserve the chance to grow, learn, and thrive—every child deserves a healthy start.
3.2 Lifesaving Sips: The Unequal Global Reach of Oral Rehydration Therapy:
Diarrhea remains one of the leading killers of children under five, yet its simplest cure—oral rehydration therapy (ORT)—is tragically uneven in its reach. This sunburst chart reveals a stark divide: while some regions have embraced ORT, saving countless young lives, others still struggle to deliver this basic, life-saving intervention.
The deeper greens show where ORT is widely available—where a packet of salts and clean water can turn despair into hope. The lighter shades expose gaps in care, where preventable deaths still occur. Each ring tells a story of progress and neglect, of innovation and inertia.
This isn’t just data—it’s a map of survival. The question isn’t whether ORT works, but whether it reaches every child who needs it. The answer could mean the difference between life and loss for millions.
3.3 Bar Chart: ORT Treatement by Gender
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import plotly.graph_objects as gocountries = sample_df["country"].unique()fig_bars = go.Figure()# my color mapcolor_map = {"Male": "rgba(22,128,60, 0.7)","Female": "rgba(179,225,173,255)","Total": "rgba(0,103,42,255)"}#for country in countries: df_filtered = sample_df[sample_df["country"] == country] df_latest = df_filtered[df_filtered["time_period"] == df_filtered["time_period"].max()] fig_bars.add_trace(go.Bar( x=df_latest["sex"], y=df_latest["obs_value"], name=country, visible=(country == countries[0]), marker=dict(color=[color_map.get(sex, "rgba(44, 160, 44, 0.7)") for sex in df_latest["sex"]]), width=0.3 ))buttons = [dict( label=country, method="update", args=[ {"visible": [c == country for c in countries]}, {"title.text": f"{country}"} ] )for country in countries]fig_bars.update_layout( updatemenus=[dict( buttons=buttons, direction="down", showactive=False, x=1.05, xanchor="left", y=1.0, yanchor="top", pad={"r": 4, "t": 4}, font=dict(size=12) ) ], title={"text": f" {countries[0]}", "x": 0.5}, xaxis_title="Gender", yaxis_title="% Receiving ORT", template="plotly_white", bargap=0.2, barmode='group')fig_bars.show()
Equal Treatment, Promising Progress
Global data shows near-identical ORT treatment rates for boys and girls with diarrhea, with girls slightly ahead in recent years—a significant achievement in health equity. This parity reflects successful interventions ensuring equal access to this life-saving care. While treatment rates are balanced, subtle differences may persist in prevention and follow-up. The equal bars represent real progress, proving that when simple, effective solutions are prioritized, gender gaps in basic healthcare can be closed. However, continued focus is needed to maintain and expand this equity across all aspects of child health
3.4 Trend Over Time for Selected Country
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import plotly.express as pximport plotly.graph_objects as godf_total = sample_df[sample_df["sex"] =="Total"]countries = df_total["country"].unique()fig = go.Figure()pattern_shapes = ["x", "/", "\\", ".", "+"]green_transparent ="rgba(0, 128, 0, 0.3)"for i, country inenumerate(countries): df_country = df_total[df_total["country"] == country] area = px.area( df_country, x="time_period", y="obs_value", title=f"ORT Trend Over Time: {country}", pattern_shape_sequence=[pattern_shapes[i %len(pattern_shapes)]], )for trace in area.data: trace.update( line=dict(color="darkgreen", width=1.5), fillcolor=green_transparent, visible=(i ==0) ) fig.add_trace(trace)buttons = [dict(label=country, method="update", args=[{"visible": [j //1== i for j inrange(len(countries))]}, {"title": f"ORT Trend Over Time: {country}"}])for i, country inenumerate(countries)]fig.update_layout( updatemenus=[dict(active=0, buttons=buttons)], xaxis_title="Year", yaxis_title="ORT Coverage (%)", template="plotly_white")fig.show()
The Journey of a Nation
Every country has its own story, especially when it comes to the health of its youngest citizens. The “Trend Over Time for Selected Country” section lets you explore each nation’s journey in combating diarrhoea among children under 5. As you select a country, you’ll follow its progress in providing Oral Rehydration Therapy (ORT), with each rise and fall on the graph representing milestones—successes, challenges, and health campaigns. Behind these data points are the lives of children profoundly impacted by ORT access.
This is more than just trend analysis; it’s a testament to each nation’s efforts toward better child health. It calls us to celebrate victories, learn from setbacks, and work towards ensuring every child has access to life-saving care. Let these trends inspire action and remind us that every child deserves a healthy life.
3.5 Scatter Plot: GDP vs ORT Coverage
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fig_gdp_scatter = px.scatter( pdf, x="GDP per capita (constant 2015 US$)", y="obs_value", color="country", title="GDP per Capita vs ORT Coverage", labels={"GDP per capita (constant 2015 US$)": "GDP per capita (2015 US$)","obs_value": "% Receiving ORT" })X = pdf[["GDP per capita (constant 2015 US$)"]]X = sm.add_constant(X)y = pdf["obs_value"]model = sm.OLS(y, X).fit()pdf["regression"] = model.predict(X)fig_gdp_scatter.add_traces(go.Scatter( x=pdf["GDP per capita (constant 2015 US$)"], y=pdf["regression"], mode="lines", name="Regression Line", line=dict(color="black", dash="dot")))fig_gdp_scatter.update_layout(template="plotly_white")fig_gdp_scatter.show()
The Intersection of Wealth and Health
The scatter plot explores the relationship between a country’s wealth, measured by GDP, and its ORT coverage for children under 5. Each dot represents a country, showing its economic strength and commitment to child health. While wealthier nations generally have higher ORT coverage, there are exceptions, with some economically challenged countries achieving impressive results. This plot highlights that GDP is not the sole determinant of healthcare success—policies, priorities, and equity also play key roles. Let it inspire action and remind us that every child, regardless of a nation’s wealth, deserves access to life-saving healthcare.
This section gives a broader view of how countries are positioned to tackle childhood diarrhoea using simple solutions like Oral Rehydration Therapy (ORT). By looking at GDP per capita, life expectancy, population size, and growth rate, we can gauge the resources, health systems, and challenges each nation faces.
It highlights which countries are better prepared and where more support or stronger policies are needed. Behind each metric is a reminder: fighting diarrhoea isn’t just about medicine — it’s about building strong systems and ensuring every child has a fair chance at a healthy life.
graph showcases the diverse journeys nations have taken in expanding access to Oral Rehydration Therapy (ORT) for children under 5. Each line represents a country, with its length showing the range of ORT coverage over time—from the lowest to the highest levels recorded. The graph reveals both progress and disparity, with some countries showing significant improvements while others still face challenges.
This graph is more than just data; it’s a story of hope and a reminder of the work still to be done. It highlights the importance of persistence, healthcare access, and the difference we can make in the lives of children. Every child deserves a healthy life, and it’s up to us to ensure they get it.
5 Conclusion
The visual narratives we’ve explored—world map, country trends, GDP vs ORT coverage, and ORT range over time—converge on one powerful message: the fight against diarrhoea in children under 5 is far from over. These charts are not just data; they reflect our progress, highlight disparities, and call us to action.
While wealth influences healthcare, it’s not the only factor. Many countries have made significant strides in ORT coverage regardless of their economic status. We’ve seen the persistence of gender disparities and tracked the unique journeys of nations striving to improve access to ORT.
At the heart of these stories are the children whose survival depends on a simple, affordable treatment. As we move forward, let these narratives inspire action, reminding us of the importance of equitable healthcare and the impact we can have on children’s lives.
Every child deserves a healthy start, and it’s up to us to make that a reality. Let’s continue the fight against diarrhoea and ensure every child has access to life-saving ORT, for a healthier future for all.